Book Image

Hands-On Simulation Modeling with Python

By : Giuseppe Ciaburro
Book Image

Hands-On Simulation Modeling with Python

By: Giuseppe Ciaburro

Overview of this book

Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python. Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.
Table of Contents (16 chapters)
Section 1: Getting Started with Numerical Simulation
Section 2: Simulation Modeling Algorithms and Techniques
Section 3: Real-World Applications

Random number generation using Python

So far, we have seen what methods can be used for generating random numbers. We have also proposed some solutions in Python code for the generation of random numbers through some universally used methods. These applications have been useful for understanding the basis on which random number generators have been made. In Python, there is a specific module for the generation of random numbers: this is the random module. Let's examine what it is.

Introducing the random module

The random module implements PRNGs for various distributions. The random module is based on the Mersenne Twister algorithm, which was originally developed to produce inputs for Monte Carlo simulations. The Mersenne Twister algorithm is a PRNG that produces almost uniform numbers suitable for a wide range of applications.

It is important to note that random numbers are generated using repeatable and predictable deterministic algorithms. They begin with a certain...